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Record W4408027705 · doi:10.1080/11926422.2025.2458884

Balancing acts and breakthroughs: Canada's journey with linguistic diversity, indigenous rights, and the UN Declaration to inspire public service innovation

2025· article· en· W4408027705 on OpenAlexaffabout
Carolyn Laude

Bibliographic record

VenueCanadian Foreign Policy Journal · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeclarationIndigenousPolitical scienceDiversity (politics)Public serviceService (business)Linguistic diversityPublic administrationMedia studiesSociologyLinguisticsLawEconomyEconomicsPhilosophy

Abstract

fetched live from OpenAlex

Canada’s involvement in the Global Task Force for the Decade of Action for Indigenous Languages and other national government initiatives underscores its commitment to preserving Indigenous languages. However, balancing Indigenous linguistic rights with official bilingualism in the federal workplace is complex. Research indicates that the bilingual language policy, aimed at reducing inequality, often puts Indigenous public servants at a disadvantage, as they feel pressured to learn another colonial language. Upholding Indigenous linguistic rights alongside official languages is essential to prevent forced assimilation and support cultural identity, guided by Article 8 in the United Nations Declaration on the Rights of Indigenous Peoples. Despite the legal recognition of Section 35 rights and the Declaration, public servants often struggle to understand and consistently apply them in their everyday duties. Canada must protect Indigenous linguistic rights to preserve Indigenous identity, culture, and knowledge. This is crucial for its leadership in Indigenous rights, reconciliation, implementing the Declaration, and promoting global social inclusion efforts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.212
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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